class_docstr: Arguments pertaining to what data we are going to input our model for training and eval.
class_docstr_tok: ["Arguments","pertaining","to","what","data","we","are","going","to","input","our","model","for","training","and","eval","."]
code: def __post_init__(self):
      if self.train_file is not None:
          extension = self.train_file.split(".")[-1]
          assert extension in [
              "csv",
              "json",
              "txt",
          ], "`train_file` should be a csv, a json or a txt file."
      if self.validation_file is not None:
          extension = self.validation_file.split(".")[-1]
          assert extension in [
              "csv",
              "json",
              "txt",
          ], "`validation_file` should be a csv, a json or a txt file."
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/examples/research_projects/mlm_wwm/run_mlm_wwm.py
imports: import json
  import logging
  import math
  import os
  import sys
  from dataclasses import dataclass, field
  from typing import Optional
  from datasets import Dataset, load_dataset
  import transformers
  from transformers import CONFIG_MAPPING, MODEL_FOR_MASKED_LM_MAPPING, AutoConfig, AutoModelForMaskedLM, AutoTokenizer, DataCollatorForWholeWordMask, HfArgumentParser, Trainer, TrainingArguments, set_seed
  from transformers.trainer_utils import get_last_checkpoint, is_main_process
label_desc: Use image node_id 1 for calling the DataTrainingArguments obj's underlying member method code with example usage: obj.__post_init__() without return types